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In this paper, we reviewed the conventional HMM based ASR including its architecture, several important problems and also some standard solutions for these problems. We have also presented another ASR system, namely, the hybrid NN/HMM system. Various discriminative training schemes including MMIE, MCE, MPE, and large margin for standard HMM system are also investigated. For the hybrid NN/HMM system, we have shown the discriminative nature of NN and how to incorporate NN into standard HMM ASR system to improve the system performance. WFST, as a new framework for decoding of HMM network, is also discussed. WFST can greatly simplify the integration of knowledge sources in speech recognition due to the uniform representation of all essential components of a standard ASR system.

We also conducted several experiments on WSJ0 based on NN/HMM system. The experiments on phone recognition show that our hybrid system outperforms the generative learning approach of conventional HMM system. However for the word level recognition experiment, we found the ML trained HMM system beats our hybrid system. Based on the experiment results, we have some analysis on our hybrid system concerning the word recognition problem and found some limitations of the training criteria of NN. Finally, we gave some possible directions to overcome these limitations in our future work.


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